SPEEDUP-AWARE CO-SCHEDULES FOR EFFICIENT WORKLOAD MANAGEMENT

Abstract

Many HPC systems run workloads comprising scientific simulations where the problem size is often fixed while model parameters are changed for each run. The speedup profile of such applications can often be determined using data from previous runs. Many applications exhibit sublinear speedups and thus only incremental improvements in execution time when larger numbers of processors are used. In this paper, we consider a workload of applications queued for execution and co-schedules that can exploit speedup profiles. More specifically, we seek co-schedules to reduce workload completion time (i.e., makespan), and total energy consumed by the system. Additionally, we explore how co-schedules may reduce average application turnaround time and thus benefit the user. We propose speedup-aware processor partitioning (SAPP) co-scheduling schemes, including optimal and greedy variants. We show that our greedy co-schedules reduce system energy, workload makespan and average application turnaround time when compared to a base scheme that runs each application on all available processors. Our experiments demonstrate that on a system with 128 processors, on average, our SAPP-greedy co-schedules can reduce makespan and turnaround time by 20% and decrease total energy consumed by 40%.